Tree Models
9. Tree Models¶
Tree-based models derive a series of if-then rules from training data, then use those rules to make predictions about new observations. Decision trees, which are the foundation of tree-based models, make predictions using a clear and easily interpretable set of rules. There are many additional tree-based models, such as random forest and gradient boosting trees, that build on the decision tree model. All of these models can be used for classification (i.e. modeling a categorical outcome) and regression (i.e. modeling a continuous outcome).